The present invention, in some embodiments thereof, relates to a system and a method for segmenting medical imaging data, such as computed tomography (CT) images and, more particularly, but not exclusively, to a system and a method for using a binary atlas of the human skeleton for segmenting medical imaging data.
Medical images are images of a human subject that are analyzed for the purposes of biological and medical research, diagnosing and treating disease, injury and birth defects. Commonly, medical images involve modalities that are able to capture data that allows imaging internal organs and tissues in a non-invasive manner. Examples of such modalities include computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, fluoroscopy, conventional x-rays, and the like. Medical images may be analogue or digital, two-dimensional or three-dimensional; however, three-dimensional modalities are digital.
When medical images are taken for diagnosis they are usually meticulously inspected by computer aided diagnosis (CAD) systems and/or trained medical practitioners, for example radiologists, to detect instances of abnormality that may be indicative of diseases. Additionally, the medical images may be used to accurately locate lesions so that treatments such as chemotherapy and radiotherapy may be precisely delivered and surgery may be effectively planned.
As medical images are usually three or four dimensional, the practitioner may step through a sequence of two-dimensional image slices at regular intervals, inspecting each slice. Thus, inspection of medical images may be tedious and prone to error. Accordingly, methods of computer aided detection (CAD) have been developed for the automatic location, registration, and segmentation. CAD may also be used to locate, characterize and segment anatomical structures.
Segmentation may be performed according to local properties of bones and/or tissues. For example, International Patent Application No. WO/2006/097911 describes a method of automatically identifying bone components in a medical image dataset of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, wherein none of the tests examine an extent to which the image radiodensity has a local maximum at or near a voxel and falls steeply going away from the local maximum in both directions along an axis; b) applying a second set of one or more tests to accept seeds as bone seeds, at least one of the tests requiring at least one voxel belonging to the seed to have a local maximum in image radiodensity at or near said voxel, with the image radiodensity falling sufficiently steeply in both directions along at least one axis; and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.